Proteome Scale-Protein Turnover Analysis Using High Resolution Mass Spectrometric Data from Stable-Isotope Labeled Plants

Abstract

Protein turnover is an important aspect of the regulation of cellular processes for organisms when responding to developmental or environmental cues. The measurement of protein turnover in plants, in contrast to that of rapidly growing unicellular organismal cultures, is made more complicated by the high degree of amino acid recycling, resulting in significant transient isotope incorporation distributions that must be dealt with computationally for high throughput analysis to be practical. An algorithm in R, ProteinTurnover, was developed to calculate protein turnover with transient stable isotope incorporation distributions in a high throughput automated manner using high resolution MS and MS/MS proteomic analysis of stable isotopically labeled plant material. ProteinTurnover extracts isotopic distribution information from raw MS data for peptides identified by MS/MS from data sets of either isotopic label dilution or incorporation experiments. Variable isotopic incorporation distributions were modeled using binomial and beta-binomial distributions to deconvolute the natural abundance, newly synthesized/partial-labeled, and fully labeled peptide distributions. Maximum likelihood estimation was performed to calculate the distribution abundance proportion of old and newly synthesized peptides. The half-life or turnover rate of each peptide was calculated from changes in the distribution abundance proportions using nonlinear regression. We applied ProteinTurnover to obtain half-lives of proteins from enriched soluble and membrane fractions from Arabidopsis roots.

Protein Degradation Rate in Arabidopsis thaliana Leaf Growth and Development

“…Most historical studies in mammals have assumed steady state to be the case in short time frames where no treatments were imposed (Price et al, 2010;Cambridge et al, 2011;Schwanhäusser et al, 2011). In plants, some reports have attempted to measure changes in protein abundance of specific targets of interest (Li et al, 2012;Lyon et al, 2016), and others have relied on linear regression across a time series to remove outliers and thus only retain information when turnover rates have not changed (Yang et al, 2010;Nelson et al, 2014;Fan et al, 2016). However, these approaches have their limitations in plants and are prone to both error and to the removal of interesting biological features of interest in the data.…”

“…As a result, studies have assessed the utility of various metabolic labels ( 2 H, 13 C, and 15 N) for the purpose in plants (Yang et al, 2010;Chen et al, 2011;Li et al, 2012). Recently, metabolic labeling with inorganic 15 N has been the most commonly used stable isotope incorporation technique to define degradation or turnover rates of organelle proteins in Arabidopsis cell culture (Nelson et al, 2013), protease targets in Arabidopsis leaf mitochondria (Huang et al, 2015;Li et al, 2016), ;500 proteins in barley (Hordeum vulgare) leaves (Nelson et al, 2014), ;200 membrane and microsomal fraction proteins from Arabidopsis roots (Fan et al, 2016), and ;500 Medicago truncatula leaf and root proteins during drought and recovery from drought (Lyon et al, 2016). One study has also followed selected protein degradation rates using 13 CO 2 in Arabidopsis leaves (Ishihara et al, 2015).…”

A reference-based protein degradation assay without global translation inhibitors

“…A variety of protein degradation assays are currently in use, including pulse-chases as well as proteomescale techniques based on mass spectrometry (19,(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48).…”

Silencing of OsCV (chloroplast vesiculation) maintained photorespiration and N assimilation in rice plants grown under elevated CO2

“…Scaffold (version Scaffold 4; http://www.proteomesoftware.com) was used to validate tandem MS‐based peptide and protein identification. The results were filtered with a false discovery rate of less than 0.5% on the peptide level and 1% on the protein level with a minimum of two unique peptides required for identification (Fan et al, ).…”